Abhijith Shetty

building legos @Clutchcanada . Former founder. Engineer forever.

[Abhijith Shetty @trystwithlife]

@ntkris @bgurley Are you using orchestration/coordination layers when you're off loading work to other models?

[Abhijith Shetty @trystwithlife]

Huge! I've been building static HTML pages to share with team mates.
One thing that the labs haven't built yet is the social side of AI/coding agents. I still can't collab with a colleague on a session. I have write a .md and then share it.
This will be a huge unlock!

[Boris Cherny @bcherny]

I've been using Artifacts in Claude Code for everything: visual explanations of tricky code, system diagrams, quick previews of a few animation options, data analyses and dashboards I share with the team. They are a game changer for how I work with Claude. Can't wait to hear what you think!

[Abhijith Shetty @trystwithlife]

Huge insights - the morning after bill is in and almost all CIOs and Eng teams are scrambling for a solution that can help continue pushing the outcomes while keeping the token cost under control.
The Uber/Lyft subsidizing works for Pro/Max accounts but enterprises have to pay!

[Gokul Rajaram @gokulr]

THE TOKEN HANGOVER

@matanSF (Matan Grinberg), CEO and co-founder of @FactoryAI , interviewed by @HarryStebbings (@20vcFund )

This is a special for me since I've been an investor in @FactoryAI since their seed round, and think Matan is a very very special founder.

Summary: Grinberg argues the next 24 months in enterprise AI are a resource-allocation problem: tokens, dollars, and people. Most CIOs are now waking up to bills they cannot justify. The fix is to spend frontier tokens only on the 10-20% of work that requires planning intelligence, run the other 80-90% on open models, and rebuild teams around load-bearing polymaths who own business outcomes. The single-frontier-monopoly fear is fading: four roughly-equivalent labs is the emerging reality, which puts pricing power back in the application layer.

1. The Token Hangover. Enterprise AI adoption ran through three phases this year: boards yelling at CEOs about AI strategy, "token maxing" with AI usage written into perf reviews, and now the morning-after bill. One CIO Grinberg spoke to was spending hundreds of thousands of dollars a month on engineers asking Opus 4.8 things like "how's it going" and "what are my macros from lunch." The frontier model became the default surface for every question, no matter how trivial. Phase 3 is the moment routing matters: every call to a frontier model needs to earn its price.

2. Resource Allocation Is the Job. For the next 24 months every C-suite is solving the same problem: how to allocate dollars, tokens, and headcount against business outcomes. Engineering teams used to be judged by features shipped per quarter, a metric with no link to revenue, market share, or retention. A logistics company adding more engineers to ship more features was always solving the wrong problem; AI made the misallocation visible. Tie every person's work to the metric that actually moves the business, then re-allocate.

3. Load-Bearing Individuals. The "10x engineer" frame measures lines of code, the wrong unit. Grinberg's unit is the load-bearing individual: the person whose absence breaks something. With AI the load-bearing few compound roughly 10,000%; the others get close to nothing, so any org enforcing one token-spend-per-engineer number is painting with too wide a brush. Average token spend per engineer will land on the same order of magnitude as their salary within three years, with a wildly bimodal distribution.

4. Frontier for Decisions Only. 80-90% of software development tasks can run on open models; the remaining 10-20% is planning, where the frontier still wins. This mirrors how human orgs work: leadership is a tiny share of total hours but decides the company's fate. The ego trap is engineers assuming their work is too important for an open model. The router decides better than the engineer, and the cost curve falls only if you wire the routing.

5. The Kirkland Mistake. Kirkland & Ellis announced a $500M, five-year internal AI build, which Grinberg reads as validation for Harvey rather than a threat. Building AI is not a law firm's core competency, and Kirkland's spend will teach them how hard it is. The general rule: just because you can build it does not mean you should, and the discipline is naming the few things you and your team own end-to-end. Outsource everything else, even when you technically know how to do it yourself.

6. Model-App Separation. When the model provider also sells the app, the incentives split: an API business wants you to spend more tokens. A healthy market keeps the application layer independent, so model providers compete on price, speed, and quality every week. Enterprises do not want to vendor-lock again; every CIO carries scars from the cloud era's three-year discount-then-jack-the-price trap. The application layer survives precisely because it forces that competition.

7. Sales as Product. Name a legendary company with a weak sales or marketing team. You can't. The Silicon Valley fallacy that research sits at the top and sales is "dirty work" produces companies that win the gold rush and then collapse when gravity returns. At Factory, engineers and salespeople sit intermixed; when sales closes, engineering says "we closed"; when engineering ships, sales says "we shipped." Atrophied sales muscles will not regrow once enterprise buyers stop saying yes to everything.

8. Polymath Era. Da Vinci, Newton, Euler could be polymaths because their fields were shallow. By the 2010s a theoretical physicist needed 50 years to reach the frontier before contributing anything new. AI collapses that catch-up time, so one person can push forward developer marketing, token-caching infrastructure, and solution engineering at once. The engineer of the future is a GM who owns marketing copy, product metrics, and sales enablement.

9. Build the Factory. Factory's name is literal: engineers in the next era design the assembly line that produces software. The DevX investments that used to scale linearly with headcount (good docs, CI/CD, linters, pre-commit hooks) now scale with the number of agents you run, which is 10x or 100x larger. Every dollar spent making agents production-ready compounds against thousands of PRs a week. Humans move up the stack, from writing code to designing the system that writes code.

10. Seal Team Six. Mandating beds in the office is a hiring failure dressed up as commitment. Grinberg's image: a basketball game judged by who sweat the most, when the scoreboard is what counts. Factory bought eight sleeps for all 30 team members at the time, because recovery is where the gains come from when work requires every ounce of brain power. If your load-bearing engineer can do their best work on two hours of sleep, they were not doing load-bearing work in the first place.

11. Four Frontier Labs. Grinberg's biggest mind-change this year: a single dominant model is unlikely, and four roughly-equivalent frontier providers is the more probable steady state. That outcome is the win for humanity. A one-lab monopoly was the dangerous scenario, and four equivalent labs is also the structural bull case for the application layer because it forces real ongoing price competition. Every CIO Grinberg meets has already decided not to throw their lot in with a single provider.

12. Dario's Self-Serving Doom. "AI will take your jobs" was the pitch that helped raise hundreds of billions, and Grinberg thinks it damaged public psychology and fed the slow-AI lobby. Watch the rhetoric flip at IPO: humans will suddenly become important again, because humans are the ones buying the stock. Founders who never needed to raise that money, like Zuckerberg and Hassabis, never made that argument. Incentives drive the labor-displacement rhetoric more than philosophy does.

[Abhijith Shetty @trystwithlife]

Voice is becoming a new surface for product interaction at scale especially consumer space!

[Aoden Teo @AodenTeoMT]

Today, we’re excited to introduce Miso One, the most emotive voice model in the world.

Miso One is an 8-billion-parameter text-to-speech model for highly expressive speech generation. It emotes like a human and responds faster than a human, with just 110 milliseconds of latency.

We’ve open-sourced the model weights, with API access coming soon.

Hear how Miso One sounds in the thread below.

[Abhijith Shetty @trystwithlife]

We haven't even touched on building headless systems for your customer's agents to discover and conduct transactions on your platform, we are probably 18-24 months out from mass personal agent adoption where all businesses will be forced to build a headless version. (3)

[Abhijith Shetty @trystwithlife]

AI models have blown open the product management function. Historically PM were only responsible for software experiences, now the scope expands to all customer facing interactions including AI agents (1)

[Abhijith Shetty @trystwithlife]

Voice is especially new form factor. Some parts stay the same: you still have to solve customer problems, remove funnel friction and create value. However, it expands from just deterministic software to building non deterministic reasoning agents + agent harnesses. (2)

[Abhijith Shetty @trystwithlife]

@garrytan Fascinating! We're going to start seeing the 10x and 100x humans.
The compounding is going to be unreal!

[Abhijith Shetty @trystwithlife]

@OpenAIDevs This is gold! Lots of insights in here

[Abhijith Shetty @trystwithlife]

@businessbarista AI-native

[Abhijith Shetty @trystwithlife]

@thsottiaux Collaborative chats inside Codex, so that colleagues can work together on features/tasks.
Social coding sessions on Codex!

[Abhijith Shetty @trystwithlife]

@thsottiaux Switching a voice agent from existing cascade architecture to a full Speech to speech architecture with tool calling, prompts, guardrails, knowledge base etc. all tailored to the S2S architecture backed by deep research on S2S.

[Abhijith Shetty @trystwithlife]

At Clutch, we're seeing this first hand - a step change in performance with the early adopters getting to >1.5x avg performance.
The change management challenge will be to bring adoption to everyone in the team, especially the bottom quartile of slow adopters.

[Abhijith Shetty @trystwithlife]

A rarely spoken effect of the 10x product velocity with coding agents is change management on 95% of any org that lives outside the AI bubble.
Shipping 50 features in 50 days is great, until you have to train a team to adopt and ramp up to that new product velocity.

[Abhijith Shetty @trystwithlife]

The outcomes will quickly create a separate league of orgs that are 10x more productive because of their high adoption rate.

[Abhijith Shetty @trystwithlife]

One consequence of democratizing programming: I've had two salespeople with no formal training or any experience with programming, show me prototypes to improve their own productivity. These folks would've been previously resigned to product/adjacent teams to build solutions.

[Abhijith Shetty @trystwithlife]

One of my favourite quotes from Aryton Senna-"You cannot overtake 15 cars in sunny weather…but you can when it's raining". I keep thinking about how AI will change everything about knowledge work as we know it.
It's a thunderstorm out there,friends and field is wide open!

[Abhijith Shetty @trystwithlife]

The last 3 months have been the most cognitively exhausting work being done. Pre coding agents the speed of decision making from one to the next was maybe days, even weeks.
Today,as @steipete wrote "Shipping at the speed of Inference" x multiple projects short circuits the brain!

[Lenny Rachitsky @lennysan]

"Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting.

I can fire up four agents in parallel and have them work on four different problems, and by 11am I am wiped out for the day.

There is a limit on human cognition. Even if you're not reviewing everything they're doing, how much you can hold in your head at one time. There's a sort of personal skill that we have to learn, which is finding our new limits. What is a responsible way for us to not burn out, and for us to use the time that we have?" @simonw

[Abhijith Shetty @trystwithlife]

Happy middle! Not too fussy about the syntax but knows enough to unleash the agent on specific problems, troubleshooting is quicker and most importantly they now have the power of software for high impact outcomes.
These people are the real winners of the agentic coding era!

[Abhijith Shetty @trystwithlife]

On average, people who would benefit most from leveraging coding agents (apart from the 10x engineer who will become 100x or 1000x): Pseudo technical folks (think generalist, tech adjacent - product, design, data maybe?)
Let me explain..

[Abhijith Shetty @trystwithlife]

On the other extreme you have the non technical people, coding agents make English the coding language but to leverage it you need to understand getting end, back end, how systems interact. You need to know where to point the most powerful canon.
Enter psuedo technical people..